River plume effects on larval yellow perch (Perca flavescens ) survival and predation in the western basin of Lake Erie
Bibliographic record
Abstract
The western basin of Lake Erie receives input from two main tributaries (Maumee and Detroit River), which differ greatly in their nutrient and sediment loading. The higher turbidity of the Maumee River plume is thought to reduce predation on early-stage juvenile yellow perch (Perca flavescens), consequently increasing their survival. For this reason, my overall objective was to evaluate the effect of the Maumee River plume on the overall recruitment of larval yellow perch to the juvenile stage. However, traditional diet analyses are not effective for evaluating larval predation rates. I therefore review genetic and non-genetic diet analysis techniques, and how they have evolved with technological advances, allowing researchers to effectively explore trophic interactions and energy movement in aquatic ecosystems. This provided a framework for my doctoral research for which I used a variety of molecular genetic techniques to estimate survival of larval yellow perch using population genetics and predation rates through stomach content analysis of predator fish. Using yellow perch microsatellite markers, I measured temporal and spatial genetic structure in larval yellow perch, while Bayesian genotype assignment provided relative larval survival estimates for yellow perch inhabiting the Maumee and Detroit River plumes. Overall, genotype assignment of Age-0 yellow perch establishes that, in the western basin of Lake Erie, larval recruitment to the juvenile stage is significantly higher for fish inhabiting the Maumee River plume relative to those in the Detroit River plume. In addition, I utilized molecular genetic techniques to accurately identify highly digested early-stage juvenile prey to the species level which was not possible with a more traditional approach (visual inspection of gut contents). Specifically, I use polymerase chain reaction (PCR), cloning and sequencing, to demonstrate the diversity of prey consumed by several freshwater fish species. Finally, using species-specific single nucleotide polymorphism (SNP) assays and microsatellite markers, I quantified predation and cannibalism experienced by larval yellow perch in the Maumee and Detroit river plumes. The combination of markers showed generally higher predation and cannibalism in the less turbid waters of the Detroit River plume, indicative of river-plume effects (possibly mediated through turbidity) reducing larval yellow perch predation mortality. My doctoral research shows the likely mechanism that river plumes in the western basin of Lake Erie contribute to larval yellow perch survival and consequently potential recruitment; however, temporal and spatial variability indicate that other factors also contribute.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".